For all experiments, the input features are 80-dimensional
log Mel-filterbank(FBank) computed on 25ms window with
10ms shift. We use the open-source WeNet toolkit[1]} to build both the vanilla hybrid CTC/attention Conformer baseline and our proposed Blockformer. We use SpecAugment[2]}, [3]} for data augmentation with the frequency mask parameter (F = 10), the time mask parameter(T=50), and the number of frequency and time masks(mF = mT = 2). We choose 4233 and 4599 characters (including \(\langle pad\rangle , \langle eos\rangle , \langle sos\rangle \)  labels) as model units for
AISHELL-1, 1400-hour internal corpus respectively.
